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ClassNotes01

# ClassNotes01 - Decision Analysis OEM 2009 ESI 6321 Applied...

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Decision Analysis OEM 2009 ESI 6321 –Applied Probability Methods in Engineering 2 Applied Probability Methods in Engineering The power of probability and statistics to model complex business decisions Topics: decision making under uncertainty regression techniques statistical quality control reliability Markov chains and queueing theory

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3 Applied Probability Methods in Engineering 8 homeworks 35% Submit via eLearning website (pdf format preferred) Quiz Prework material 1 hr, January 12, 10% Midterm Material of December and January classes 2 hrs, February 16, 25% Final Material of February and March classes 2 hrs, April 19, 30% 4 Applied Probability Methods in Engineering Teaching assistants Saed Alizamir, [email protected] Jon Lowe, [email protected] Preferred method of communication E-mail On-line TA “office hours” Will be scheduled in the week before each homework is due
5 Decision Tree Models Bill Sampras is a first year Master’s student thinking about summer employment. On a flight to school he meets Vanessa, VP for a major banking firm She tells him she’d like to discuss summer employment opportunities in mid-November The company Bill left promised him a summer position The offer is only good until the end of October Alternatively he could seek other employment He feels all available opportunities would offer similar learning and networking experiences His only decision criterion therefore is salary 6 Decision Trees A decision tree is a systematic way of representing decisions and uncertainties. We will represent a decision with a box called a decision node Different decisions are represented by branches emanating from the decision node For example: Bill’s initial decision node is A Accept current employer’s offer Reject current employer’s offer

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7 Decision Trees After a decision is made, different, uncertain outcomes might occur An uncertain event is represented by a circle called an event node A branch emanating from an event node implies one of possibly several different outcomes For example: if Bill doesn’t accept his current employer’s offer, he should next see whether Vanessa offers him a job. This results in an event node A Accept current employer’s offer Reject current employer’s offer B Offer from Vanessa No Offer from Vanessa 8 Decision Trees Outcome branches coming from an event node must be mutually exclusive and collectively exhaustive. Mutually exclusive: No two separate outcomes can occur simultaneously Collectively exhaustive: Every possible outcome is represented by a branch Let’s continue to build Bill’s decision tree.
9 Bill’s Decision Tree If Vanessa offers Bill a job he must decide whether to accept it, i.e., we introduce another decision node A Accept current employer’s offer Reject current employer’s offer B Offer from Vanessa No Offer from

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